{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Linear Model Hypeparameter Tuning, Ridge and LASSO Regression\n",
    "\n",
    "#### Michael J. Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a set of interactive dashboards to explore the use of a hyperparameter to tune linear machine learning models. The hyperparameter controls the degree of fit to the data, known as model regularization. We will visualize this for both ridge and LASSO regression. \n",
    "\n",
    "To assist you with background content, I have a lectures available with linked interactive codes and well-documented workflows in Python:\n",
    "\n",
    "* [Linear Regression](https://youtu.be/0fzbyhWiP84) \n",
    "* [Ridge Regression](https://youtu.be/pMGO40yXZ5Y?si=FwAFWWSqd10SV19h) \n",
    "* [LASSO Regression](https://youtu.be/cVFYhlCCI_8?si=IoAmCEKGvzlGULON)\n",
    "\n",
    "I also have lectures on:\n",
    "\n",
    "* [Machine Learning Basics](https://youtu.be/zOUM_AnI1DQ?si=L1FxPRc-n9y8Yuk6)\n",
    "* [Machine Learning Model Generalization and Overfit](https://youtu.be/GGoNTMrCBbk?si=itx1p3G6PG7witpe)\n",
    "* [Machine Learning Model Norms](https://youtu.be/JmxGlrurQp0?si=FPC7-Et66bWRMhFl)\n",
    "    \n",
    "these are all part of my [Machine Learning](https://www.youtube.com/playlist?list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf) course. Note, all recorded lectures, interactive and well-documented workflow demononstrations are available on my GitHub repository [GeostatsGuy's Python Numerical Demos](https://github.com/GeostatsGuy/PythonNumericalDemos). \n",
    "\n",
    "#### Linear Regression\n",
    "\n",
    "Linear regression for prediction.  Here are some key aspects of linear regression:\n",
    "\n",
    "**Parametric Model**\n",
    "\n",
    "* the fit model is a simple weighted linear additive model based on all the available features, $x_1,\\ldots,x_m$.\n",
    "\n",
    "* the general form of the multilinear regression model takes the form of $y = \\sum_{\\alpha = 1}^m b_{\\alpha} X_{\\alpha} + b_0$\n",
    "\n",
    "* the specific form of the linear regression model takes the form $y = b_1 x + b_0$\n",
    "\n",
    "**Least Squares**\n",
    "\n",
    "* least squares optimization is applied to select the model parameters, $b_1,\\ldots,b_m,b_0$ \n",
    "\n",
    "* we minize the error over the trainind data $\\sum_{i=1}^n (y_i - (\\sum_{\\alpha = 1}^m b_{\\alpha} x_{\\alpha} + b_0))^2$\n",
    "\n",
    "* this could be simplified as the sum of square error over the training data, $\\sum_{i=1}^n (\\Delta y_i)^2$\n",
    "\n",
    "**Assumptions**\n",
    "\n",
    "* **Error-free** - predictor variables are error free, not random variables \n",
    "* **Linearity** - response is linear combination of feature(s)\n",
    "* **Constant Variance** - error in response is constant over predictor(s) value\n",
    "* **Independence of Error** - error in response are uncorrelated with each other\n",
    "* **No multicollinearity** - none of the features are redundant with other features \n",
    "\n",
    "#### Ridge Regression\n",
    "\n",
    "With ridge regression we add a hyperparameter, $\\lambda$, to our minimization, with a L2 shrinkage penalty term, $\\sum_{j=1}^m b_{\\alpha}^2$.\n",
    "\n",
    "\\begin{equation}\n",
    "\\sum_{i=1}^n (y_i - (\\sum_{\\alpha = 1}^m b_{\\alpha} x_{\\alpha} + b_0))^2 + \\lambda \\sum_{j=1}^m b_{\\alpha}^2\n",
    "\\end{equation}\n",
    "\n",
    "As a result ridge regression has 2 criteria:\n",
    "\n",
    "* set the model parameters to minimize the error with training data\n",
    "\n",
    "* shrink the estimates of the slope parameters towards zero\n",
    "\n",
    "Note: the intercept is not affected by lambda.\n",
    "\n",
    "The $\\lambda$ is a hyperparameter that controls the degree of fit of the model and may be related to the model variance and bias trade-off.\n",
    "\n",
    "* for $\\lambda \\rightarrow 0$ the solution approaches linear regression, there is no bias (relative to a linear model fit), but the variance is high\n",
    "\n",
    "* as $\\lambda$ increases the model variance decreases and the model bias increases\n",
    "\n",
    "* for $\\lambda \\rightarrow \\infty$ the coefficients approach 0.0 and the model approaches the global mean\n",
    "\n",
    "#### Lasso Regression\n",
    "\n",
    "With the lasso we add a hyperparameter, $\\lambda$, to our minimization, with a L1 shrinkage penalty term.\n",
    "\n",
    "\\begin{equation}\n",
    "\\sum_{i=1}^n \\left(y_i - \\left(\\sum_{\\alpha = 1}^m b_{\\alpha} x_{\\alpha} + b_0 \\right) \\right)^2 + \\lambda \\sum_{j=1}^m |b_{\\alpha}|\n",
    "\\end{equation}\n",
    "\n",
    "As a result the lasso has 2 criteria:\n",
    "\n",
    "1. set the model parameters to minimize the error with training data\n",
    "\n",
    "2. shrink the estimates of the slope parameters towards zero. Note: the intercept is not affected by the lambda, $\\lambda$, hyperparameter.\n",
    "\n",
    "Note the only difference between the lasso and ridge regression is:\n",
    "\n",
    "* for the lasso the shrinkage term is posed as an $\\ell_1$ penalty ($\\lambda \\sum_{\\alpha=1}^m |b_{\\alpha}|$) \n",
    "\n",
    "* for ridge regression the shrinkage term is posed as an $\\ell_2$ penalty ($\\lambda \\sum_{\\alpha=1}^m \\left(b_{\\alpha}\\right)^2$).\n",
    "\n",
    "While both ridge regression and the lasso shrink the model parameters ($b_{\\alpha}, \\alpha = 1,\\ldots,m$) towards zero:\n",
    "\n",
    "* the lasso parameters reach zero at different rates for each predictor feature as the lambda, $\\lambda$, hyperparameter increases. \n",
    "\n",
    "* as a result the lasso provides a method for feature ranking and selection!\n",
    "\n",
    "The lambda, $\\lambda$, hyperparameter controls the degree of fit of the model and may be related to the model variance and bias trade-off.\n",
    "\n",
    "* for $\\lambda \\rightarrow 0$ the prediction model approaches linear regression, there is lower model bias, but the model variance is higher\n",
    "\n",
    "* as $\\lambda$ increases the model variance decreases and the model bias increases\n",
    "\n",
    "* for $\\lambda \\rightarrow \\infty$ the coefficients all become 0.0 and the model is the global mean\n",
    "\n",
    "\n",
    "#### Other Resources\n",
    "\n",
    "This is a tutorial / demonstration of **Linear Regression**.  In $Python$, the $SciPy$ package, specifically the $Stats$ functions (https://docs.scipy.org/doc/scipy/reference/stats.html) provide excellent tools for efficient use of statistics.  \n",
    "I have previously provided this example in R and posted it on GitHub:\n",
    "\n",
    "* [Linear Regression in R](https://github.com/GeostatsGuy/geostatsr/blob/master/linear_regression_demo_v2.R)\n",
    "* [Linear Regression in R markdown](https://github.com/GeostatsGuy/geostatsr/blob/master/linear_regression_demo_v2.Rmd) with docs \n",
    "* [Linear Regression in R document](https://github.com/GeostatsGuy/geostatsr/blob/master/linear_regression_demo_v2.html) knit as an HTML document\n",
    "\n",
    "and also in Excel:\n",
    "\n",
    "* [Linear Regression in Excel](https://github.com/GeostatsGuy/ExcelNumericalDemos/blob/master/Linear_Regression_Demo_v2.xlsx)\n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You may want to copy the data file to your working directory. They are available here:\n",
    "\n",
    "* Tabular data - [Density_Por_data.csv](https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/Density_Por_data.csv).\n",
    "\n",
    "or you can use the code below to load the data directly from my GitHub [GeoDataSets](https://github.com/GeostatsGuy/GeoDataSets) repository.\n",
    "\n",
    "#### Import Required Packages\n",
    "\n",
    "Let's import the GeostatsPy package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "supress_warnings = True\n",
    "import math\n",
    "import os                                                   # to set current working directory \n",
    "import numpy as np                                          # arrays and matrix math\n",
    "import scipy.stats as st                                    # statistical methods\n",
    "import pandas as pd                                         # DataFrames\n",
    "import matplotlib.colors as colors                          # color bar normalization                                          \n",
    "import matplotlib.pyplot as plt                             # for plotting\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "from sklearn.linear_model import LinearRegression           # linear regression\n",
    "from sklearn.linear_model import Ridge                      # ridge regression\n",
    "from sklearn.linear_model import Lasso                      # lASSO regression\n",
    "from ipywidgets import interactive                          # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox\n",
    "cmap = plt.cm.inferno                                       # default color bar, no bias and friendly for color vision defeciency\n",
    "plt.rc('axes', axisbelow=True)                              # grid behind plotting elements\n",
    "seed = 73073                                                # random number seed\n",
    "if supress_warnings == True:\n",
    "    import warnings                                         # supress any warnings for this demonstration\n",
    "    warnings.filterwarnings('ignore')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs. \n",
    "\n",
    "#### Declare functions\n",
    "\n",
    "Let's define a couple of functions to streamline plotting correlation matrices and visualization of a decision tree regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_grid():\n",
    "    plt.gca().grid(True, which='major',linewidth = 1.0); plt.gca().grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "    plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "    plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Working Directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see below) data file in this working directory.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"C:\\PGE337\")                                       # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a Dataset\n",
    "\n",
    "Let's make a simple dataset with the following characteristics:\n",
    "\n",
    "* response feature, $Y$, is a linear combination of predictor features, $X_1$ and $X_2$\n",
    "* intercept term, $b_0$, is 0.0 and can be neglected, for ease of model parameter space visualization (2D only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 50; seed = 73073\n",
    "mean_x1 = 10.0; mean_x2 = 10.0; var_x1 = 4.0; var_x2 = 4.0; cov12 = 2.5\n",
    "db1 = 3.0; db2 = 2.0; eps_std = 0.5\n",
    "cov = np.array([[var_x1,cov12],[cov12,var_x2]])\n",
    "np.random.seed(seed = seed)\n",
    "X = np.random.multivariate_normal(mean = [mean_x1,mean_x2],cov = cov, size = n)\n",
    "X1, X2 = np.split(X,indices_or_sections = 2,axis=1)\n",
    "# y = b1*X1 + b2*X2 + np.random.normal(loc = 0.0,scale = eps_std,size = n)\n",
    "y = np.reshape(db1*X1 + db2*X2,newshape=[n]) + np.reshape(np.random.normal(loc = 0.0,scale = eps_std,size = n),newshape=[n])\n",
    "\n",
    "sc = plt.scatter(X1,X2,c=y,edgecolor='black',cmap = plt.cm.inferno,vmin=20.0,vmax=60.0)\n",
    "plt.xlim([5,15]); plt.ylim([5,15]); add_grid(); plt.xlabel('$X_1$'); plt.ylabel('$X_2$'); plt.title('Synthetic Linear Data')\n",
    "cbar = plt.colorbar(sc, orientation=\"vertical\", ticks=np.linspace(20, 60, 10))\n",
    "cbar.set_label('$Y$', rotation=270, labelpad=20)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.2, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the Solution Space\n",
    "\n",
    "Sample of mesh of possible linear regression model parameters and calculate the MSE over all data.\n",
    "\n",
    "* Note we are not attemption train and test split for model hyperparameter tuning. While we could add a hyper parameter through regularization with LASSO or ridge regression, we just want to visualize this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clam = 0.0 \n",
    "\n",
    "linear = LinearRegression(fit_intercept = False).fit(X, y)   # fit a linear model\n",
    "linear_b1, linear_b2 = linear.coef_\n",
    "\n",
    "ridge = Ridge(alpha=clam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "ridge_b1, ridge_b2 = ridge.coef_\n",
    "\n",
    "L = 10\n",
    "mult_b1 = np.zeros(L); mult_b2 = np.zeros(L); mult_lam = np.zeros(L); \n",
    "for i,lam in enumerate(np.logspace(-4,6,L)):\n",
    "    ridge = Ridge(alpha=lam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "    mult_b1[i], mult_b2[i] = ridge.coef_; mult_lam[i] = lam\n",
    "    \n",
    "vmin = 0.0; vmax = 100.0; #vmax = np.max(MSE_mat)            # set min and max MSE for visualization\n",
    "norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "nstep = 200; RSS_mat = np.zeros([nstep,nstep])               # set up the madel parameter mesh\n",
    "reg_mat = np.zeros([nstep,nstep]); loss_mat = np.zeros([nstep,nstep])  \n",
    "sb1 = -7.0; eb1 = 7.0; stepb1 = (eb1-sb1)/nstep;         \n",
    "sb2 = -7.0; eb2 = 7.0; stepb2 = (eb2-sb2)/nstep;\n",
    "b1_vector = np.arange(sb1, eb1, stepb1); b2_vector = np.arange(eb2, sb2, -1*stepb2)\n",
    "b1_mat, b2_mat = np.meshgrid(b1_vector, b2_vector)\n",
    "\n",
    "for ib1, b1 in enumerate(b1_vector):                        # calculate the MSE for all possible model parameters\n",
    "    for ib2, b2 in enumerate(b2_vector):\n",
    "        y_hat = np.reshape(b1 * X1 + b2 * X2,newshape=[n])\n",
    "        RSS_mat[ib2,ib1] =((y - y_hat) ** 2).sum()\n",
    "        reg_mat[ib2,ib1] = b1*b1 + b2*b2\n",
    "        loss_mat[ib2,ib1] = RSS_mat[ib2,ib1]+clam*reg_mat[ib2,ib1]\n",
    "\n",
    "plt.subplot(131)\n",
    "vmin = np.percentile(RSS_mat.flatten(),q=1); vmax = np.percentile(RSS_mat.flatten(),q=40)\n",
    "lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "im = plt.imshow(RSS_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                cmap = plt.cm.inferno_r,zorder=1)\n",
    "plt.contour(b1_mat,b2_mat,RSS_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(3,0.3,5),\n",
    "            colors='black',alpha=0.7,zorder=10)\n",
    "plt.scatter([linear_b1],[linear_b2],color='black',marker='x',s=30,zorder=100)\n",
    "plt.title('Ridge RSS L2 Only and Optimal Model Parameters')\n",
    "plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "\n",
    "plt.subplot(132)\n",
    "vmin = np.percentile(reg_mat.flatten(),q=1); vmax = np.percentile(reg_mat.flatten(),q=40)\n",
    "lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "im = plt.imshow(reg_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                cmap = plt.cm.inferno_r,zorder=1)\n",
    "plt.contour(b1_mat,b2_mat,reg_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(3,0.3,5),\n",
    "            colors='black',alpha=0.7,zorder=10)\n",
    "plt.scatter(0.0,0.0,color='black',marker='x',s=30,zorder=100)\n",
    "plt.title('Ridge Shrinkage L2 Only and Optimal Model Parameters')\n",
    "plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "\n",
    "plt.subplot(133)\n",
    "vmin = np.percentile(loss_mat.flatten(),q=1); vmax = np.percentile(loss_mat.flatten(),q=40)\n",
    "lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "im = plt.imshow(loss_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                cmap = plt.cm.inferno_r,zorder=1)\n",
    "plt.contour(b1_mat,b2_mat,loss_mat,levels=np.logspace(lvmin,lvmax,5),colors='black',alpha=1.0,\n",
    "            linewidths=np.linspace(2,0.3,5),zorder=10)\n",
    "plt.scatter([linear_b1],[linear_b2],color='grey',marker='x',s=30,zorder=100)\n",
    "plt.plot(mult_b1,mult_b2,color='grey',lw=1,ls='--')\n",
    "plt.scatter(0.0,0.0,color='grey',marker='x',s=30,zorder=100)\n",
    "\n",
    "plt.scatter([ridge_b1],[ridge_b2],color='black',marker='x',s=30,zorder=100)\n",
    "plt.title('Ridge RSS L2 + Shrinkage L2 and Regularized Model Parameters')\n",
    "plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.1); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Dashboard - Ridge Regression Loss Surface\n",
    "\n",
    "Let's start by visualizing the change in loss surface for ridge regression as we change the hyperparameter, $\\lambda$.\n",
    "\n",
    "* we use on 2 predictor feature and assume the intercept is 0.0 ($b_0 = 0.0$) so we can conveniently visualize the entire model parameter space in 2D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = widgets.Text(value='                                       Ridge Regression Regularization Demo, Michael Pyrcz, Professor, The University of Texas at Austin',\n",
    "        layout=Layout(width='950px', height='30px'))\n",
    "# P_happening_label = widgets.Text(value='Probability of Happening',layout=Layout(width='50px',height='30px',line-size='0 px'))\n",
    "clam = widgets.FloatLogSlider(min=1, max = 6, value=0, step = 0.25,description = r'$\\lambda$',orientation='horizontal', \n",
    "        style = {'description_width':'initial','button_color':'green'},layout=Layout(width='900px',height='40px'),\n",
    "        continuous_update=False,readout_format='.0f')\n",
    "\n",
    "ui_summary = widgets.HBox([clam],)\n",
    "ui_summary1 = widgets.VBox([l,ui_summary],)\n",
    "\n",
    "def run_plot_summary(clam):    \n",
    "        \n",
    "    linear = LinearRegression(fit_intercept = False).fit(X, y)   # fit a linear model\n",
    "    linear_b1, linear_b2 = linear.coef_\n",
    "    \n",
    "    ridge = Ridge(alpha=clam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "    ridge_b1, ridge_b2 = ridge.coef_\n",
    "    \n",
    "    L = 10\n",
    "    mult_b1 = np.zeros(L); mult_b2 = np.zeros(L); mult_lam = np.zeros(L); \n",
    "    for i,lam in enumerate(np.logspace(-4,6,L)):\n",
    "        ridge = Ridge(alpha=lam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "        mult_b1[i], mult_b2[i] = ridge.coef_; mult_lam[i] = lam\n",
    "        \n",
    "    vmin = 0.0; vmax = 100.0; #vmax = np.max(MSE_mat)            # set min and max MSE for visualization\n",
    "    norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "    nstep = 200; RSS_mat = np.zeros([nstep,nstep])               # set up the madel parameter mesh\n",
    "    reg_mat = np.zeros([nstep,nstep]); loss_mat = np.zeros([nstep,nstep])  \n",
    "    sb1 = -7.0; eb1 = 7.0; stepb1 = (eb1-sb1)/nstep;         \n",
    "    sb2 = -7.0; eb2 = 7.0; stepb2 = (eb2-sb2)/nstep;\n",
    "    b1_vector = np.arange(sb1, eb1, stepb1); b2_vector = np.arange(eb2, sb2, -1*stepb2)\n",
    "    b1_mat, b2_mat = np.meshgrid(b1_vector, b2_vector)\n",
    "    \n",
    "    for ib1, b1 in enumerate(b1_vector):                        # calculate the MSE for all possible model parameters\n",
    "        for ib2, b2 in enumerate(b2_vector):\n",
    "            y_hat = np.reshape(b1 * X1 + b2 * X2,newshape=[n])\n",
    "            RSS_mat[ib2,ib1] =((y - y_hat) ** 2).sum()\n",
    "            reg_mat[ib2,ib1] = b1*b1 + b2*b2\n",
    "            loss_mat[ib2,ib1] = RSS_mat[ib2,ib1] + clam*reg_mat[ib2,ib1]\n",
    "    \n",
    "    plt.subplot(131)\n",
    "    vmin = np.percentile(RSS_mat.flatten(),q=1); vmax = np.percentile(RSS_mat.flatten(),q=40)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    im = plt.imshow(RSS_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                    cmap = plt.cm.inferno_r,zorder=1)\n",
    "    plt.contour(b1_mat,b2_mat,RSS_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(3,0.3,5),\n",
    "                colors='black',alpha=0.7,zorder=10)\n",
    "    plt.scatter([linear_b1],[linear_b2],color='black',marker='x',s=30,zorder=100)\n",
    "    plt.title('Ridge RSS L2 Only and Optimal Model Parameters')\n",
    "    plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "    \n",
    "    plt.subplot(132)\n",
    "    vmin = np.percentile(reg_mat.flatten(),q=1); vmax = np.percentile(reg_mat.flatten(),q=40)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    im = plt.imshow(reg_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                    cmap = plt.cm.inferno_r,zorder=1)\n",
    "    plt.contour(b1_mat,b2_mat,reg_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(3,0.3,5),\n",
    "                colors='black',alpha=0.7,zorder=10)\n",
    "    plt.scatter(0.0,0.0,color='black',marker='x',s=30,zorder=100)\n",
    "    plt.title('Ridge Shrinkage L2 Only and Optimal Model Parameters')\n",
    "    plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "    \n",
    "    plt.subplot(133)\n",
    "    vmin = np.percentile(loss_mat.flatten(),q=1); vmax = np.percentile(loss_mat.flatten(),q=40)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    im = plt.imshow(loss_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                    cmap = plt.cm.inferno_r,zorder=1)\n",
    "    plt.contour(b1_mat,b2_mat,loss_mat,levels=np.logspace(lvmin,lvmax,5),colors='black',alpha=1.0,\n",
    "                linewidths=np.linspace(2,0.3,5),zorder=10)\n",
    "    plt.scatter([linear_b1],[linear_b2],color='grey',marker='x',s=30,zorder=100)\n",
    "    plt.plot(mult_b1,mult_b2,color='grey',lw=1,ls='--')\n",
    "    plt.scatter(0.0,0.0,color='grey',marker='x',s=30,zorder=100)\n",
    "    \n",
    "    plt.scatter([ridge_b1],[ridge_b2],color='black',marker='x',s=30,zorder=100)\n",
    "    plt.title('Ridge RSS L2 + Shrinkage L2 and Regularized Model Parameters')\n",
    "    plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.1); plt.show()\n",
    "    \n",
    "interactive_plot_summary = widgets.interactive_output(run_plot_summary, {'clam':clam,})\n",
    "interactive_plot_summary.clear_output(wait = True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Ridge Regression Loss Function with Variable $\\lambda$ Demonstration\n",
    "\n",
    "* change the $\\lambda$ hyperparameter and watch the loss surface change and the solution shift from linear regression to the global mean, all model parameters, $b_{\\alpha} = 0.0, \\alpha = 1,\\ldots,m$. This demonstration based on 2 predictor features dataset.\n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03458204143a4e8588512e0814d8099c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                       Ridge Regression Regularization Demo, Michae…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "766fcd6b21b5496383a1c8c6155d0ef9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui_summary1, interactive_plot_summary)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Dashboard - LASSO Regression Loss Surface\n",
    "\n",
    "Let's start by visualizing the change in loss surface for LASSO regression as we change the hyperparameter, $\\lambda$.\n",
    "\n",
    "* we use on 2 predictor feature and assume the intercept is 0.0 ($b_0 = 0.0$) so we can conveniently visualize the entire model parameter space in 2D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = widgets.Text(value='                                       LASSO Regression Regularization Demo, Michael Pyrcz, Professor, The University of Texas at Austin',\n",
    "        layout=Layout(width='950px', height='30px'))\n",
    "# P_happening_label = widgets.Text(value='Probability of Happening',layout=Layout(width='50px',height='30px',line-size='0 px'))\n",
    "clam = widgets.FloatSlider(min=10, max = 3000, value=0, step = 35,description = r'$\\lambda$',orientation='horizontal', \n",
    "        style = {'description_width':'initial','button_color':'green'},layout=Layout(width='900px',height='40px'),\n",
    "        continuous_update=False,readout_format='.0f')\n",
    "\n",
    "ui_summary = widgets.HBox([clam],)\n",
    "ui_summary3 = widgets.VBox([l,ui_summary],)\n",
    "\n",
    "def run_plot_summary3(clam):    \n",
    "        \n",
    "    linear = LinearRegression(fit_intercept = False).fit(X, y)   # fit a linear model\n",
    "    linear_b1, linear_b2 = linear.coef_\n",
    "    \n",
    "    lasso = Lasso(alpha=clam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "    lasso_b1, lasso_b2 = lasso.coef_\n",
    "    \n",
    "    L = 10\n",
    "    mult_b1 = np.zeros(L); mult_b2 = np.zeros(L); mult_lam = np.zeros(L); \n",
    "    for i,lam in enumerate(np.linspace(10,600,L)):\n",
    "        lasso = Lasso(alpha=lam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "        mult_b1[i], mult_b2[i] = lasso.coef_; mult_lam[i] = lam\n",
    "        \n",
    "    vmin = 0.0; vmax = 100.0; #vmax = np.max(MSE_mat)            # set min and max MSE for visualization\n",
    "    norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "    nstep = 200; RSS_mat = np.zeros([nstep,nstep])               # set up the madel parameter mesh\n",
    "    reg_mat = np.zeros([nstep,nstep]); loss_mat = np.zeros([nstep,nstep])  \n",
    "    sb1 = -7.0; eb1 = 7.0; stepb1 = (eb1-sb1)/nstep;         \n",
    "    sb2 = -7.0; eb2 = 7.0; stepb2 = (eb2-sb2)/nstep;\n",
    "    b1_vector = np.arange(sb1, eb1, stepb1); b2_vector = np.arange(eb2, sb2, -1*stepb2)\n",
    "    b1_mat, b2_mat = np.meshgrid(b1_vector, b2_vector)\n",
    "    \n",
    "    for ib1, b1 in enumerate(b1_vector):                        # calculate the MSE for all possible model parameters\n",
    "        for ib2, b2 in enumerate(b2_vector):\n",
    "            y_hat = np.reshape(b1 * X1 + b2 * X2,newshape=[n])\n",
    "            RSS_mat[ib2,ib1] =((y - y_hat) ** 2).sum()\n",
    "            reg_mat[ib2,ib1] = abs(b1) + abs(b2)\n",
    "            loss_mat[ib2,ib1] = (1/len(y))*RSS_mat[ib2,ib1] + 2*clam*reg_mat[ib2,ib1]\n",
    "    \n",
    "    plt.subplot(131)\n",
    "    vmin = np.percentile(RSS_mat.flatten(),q=1); vmax = np.percentile(RSS_mat.flatten(),q=40)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    im = plt.imshow(RSS_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                    cmap = plt.cm.inferno_r,zorder=1)\n",
    "    plt.contour(b1_mat,b2_mat,RSS_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(3,0.3,5),\n",
    "                colors='black',alpha=0.7,zorder=10)\n",
    "    plt.scatter([linear_b1],[linear_b2],color='black',marker='x',s=30,zorder=100)\n",
    "    plt.title('Ridge RSS L2 Only and Optimal Model Parameters')\n",
    "    plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "    \n",
    "    plt.subplot(132)\n",
    "    vmin = np.percentile(reg_mat.flatten(),q=1); vmax = np.percentile(reg_mat.flatten(),q=40)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    im = plt.imshow(reg_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                    cmap = plt.cm.inferno_r,zorder=1)\n",
    "    plt.contour(b1_mat,b2_mat,reg_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(3,0.3,5),\n",
    "                colors='black',alpha=0.7,zorder=10)\n",
    "    plt.scatter(0.0,0.0,color='black',marker='x',s=30,zorder=100)\n",
    "    plt.title('Ridge Shrinkage L2 Only and Optimal Model Parameters')\n",
    "    plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "    \n",
    "    plt.subplot(133)\n",
    "    vmin = np.percentile(loss_mat.flatten(),q=1); vmax = np.percentile(loss_mat.flatten(),q=40)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    im = plt.imshow(loss_mat,interpolation = None,extent = [sb1,eb1,sb2,eb2],alpha=0.1,vmin=vmin,vmax=vmax,\n",
    "                    cmap = plt.cm.inferno_r,zorder=1)\n",
    "    plt.contour(b1_mat,b2_mat,loss_mat,levels=np.logspace(lvmin,lvmax,5),colors='black',alpha=1.0,\n",
    "                linewidths=np.linspace(2,0.3,5),zorder=10)\n",
    "    plt.scatter([linear_b1],[linear_b2],color='grey',marker='x',s=30,zorder=100)\n",
    "    plt.plot(mult_b1,mult_b2,color='grey',lw=1,ls='--')\n",
    "    plt.scatter(0.0,0.0,color='grey',marker='x',s=30,zorder=100)\n",
    "    \n",
    "    plt.scatter([lasso_b1],[lasso_b2],color='black',marker='x',s=30,zorder=100)\n",
    "    plt.title('Ridge RSS L2 + Shrinkage L2 and Regularized Model Parameters')\n",
    "    plt.xlabel('b1'); plt.ylabel('b2'); add_grid() \n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.1); plt.show()\n",
    "    \n",
    "interactive_plot_summary3 = widgets.interactive_output(run_plot_summary3, {'clam':clam,})\n",
    "interactive_plot_summary3.clear_output(wait = True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive LASSO Regression Loss Function with Variable $\\lambda$ Demonstration\n",
    "\n",
    "* change the $\\lambda$ hyperparameter and watch the loss surface change and the solution shift from linear regression to the global mean, all model parameters, $b_{\\alpha} = 0.0, \\alpha = 1,\\ldots,m$. This demonstration based on 2 predictor features dataset.\n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4d6eae7a0fa482db6de110e362de145",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                       LASSO Regression Regularization Demo, Michae…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "612c0a6f9d4442368f66607b0884657e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 3 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui_summary3, interactive_plot_summary3)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obeserve the shift from OLS only to regularizaion only loss surfaces as the hyperparameter, $\\lambda$, increases.\n",
    "\n",
    "#### Interactive Dashboard - Ridge vs. LASSO OLS vs. Regularization\n",
    "\n",
    "Let's start by visualizing the change in OLS vs. regularization components of the loss function for ridge regression and LASSO regression as we change the hyperparameter, $\\lambda$.\n",
    "\n",
    "* we use on 2 predictor feature and assume the intercept is 0.0 ($b_0 = 0.0$) so we can conveniently visualize the entire model parameter space in 2D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = widgets.Text(value='                                       Ridge L2 and LASSO L1 Regression Regularization Demo, Michael Pyrcz, Professor, The University of Texas at Austin',\n",
    "        layout=Layout(width='950px', height='30px'))\n",
    "# P_happening_label = widgets.Text(value='Probability of Happening',layout=Layout(width='50px',height='30px',line-size='0 px'))\n",
    "slam = widgets.IntSlider(min=1, max = 20, value=1, step = 1.0,description = r'$\\lambda$ level',orientation='horizontal', \n",
    "        style = {'description_width':'initial','button_color':'green'},layout=Layout(width='900px',height='40px'),\n",
    "        continuous_update=False,readout_format='.0f')\n",
    "\n",
    "ui_summary = widgets.HBox([slam],)\n",
    "ui_summary2 = widgets.VBox([l,ui_summary],)\n",
    "\n",
    "def run_plot_summary2(slam):   \n",
    "    ridge_lam_mat = np.logspace(1,6,20); lasso_lam_mat = np.linspace(1,600,20)\n",
    "    ridge_lam = ridge_lam_mat[slam-1]; lasso_lam = lasso_lam_mat[slam-1];\n",
    "    \n",
    "    # calculate paths over multiple hyperparameters\n",
    "    L = 100\n",
    "    ridge_mult_b1 = np.zeros(L); ridge_mult_b2 = np.zeros(L); ridge_mult_lam = np.zeros(L) \n",
    "    lasso_mult_b1 = np.zeros(L); lasso_mult_b2 = np.zeros(L); lasso_mult_lam = np.zeros(L)\n",
    "    for i,lam in enumerate(np.logspace(-4,6,L)):\n",
    "        ridge = Ridge(alpha=lam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "        ridge_mult_b1[i], ridge_mult_b2[i] = ridge.coef_; ridge_mult_lam[i] = lam\n",
    "        \n",
    "    for i,lam in enumerate(np.logspace(0,3,L)):\n",
    "        lasso = Lasso(alpha=lam,fit_intercept = False).fit(X, y)     # fit a LASSO model\n",
    "        lasso_mult_b1[i], lasso_mult_b2[i] = lasso.coef_; lasso_mult_lam[i] = lam\n",
    "        \n",
    "    # fit linear, Ridge and LASSO models\n",
    "    linear = LinearRegression(fit_intercept = False).fit(X, y)   # fit a linear model\n",
    "    linear_b1, linear_b2 = linear.coef_\n",
    "    \n",
    "    ridge = Ridge(alpha=ridge_lam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "    ridge_b1, ridge_b2 = ridge.coef_\n",
    "    \n",
    "    lasso = Lasso(alpha=lasso_lam,fit_intercept = False).fit(X, y)     # fit a ridge model\n",
    "    lasso_b1, lasso_b2 = lasso.coef_    \n",
    "    \n",
    "    vmin = 0.0; vmax = 100.0; #vmax = np.max(MSE_mat)            # set min and max MSE for visualization\n",
    "    norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "    nstep = 200; RSS_mat = np.zeros([nstep,nstep])               # set up the madel parameter mesh\n",
    "    ridge_reg_mat = np.zeros([nstep,nstep]); ridge_loss_mat = np.zeros([nstep,nstep])  \n",
    "    lasso_reg_mat = np.zeros([nstep,nstep]); lasso_loss_mat = np.zeros([nstep,nstep])  \n",
    "    sb1 = -7.0; eb1 = 7.0; stepb1 = (eb1-sb1)/nstep;         \n",
    "    sb2 = -7.0; eb2 = 7.0; stepb2 = (eb2-sb2)/nstep;\n",
    "    b1_vector = np.arange(sb1, eb1, stepb1); b2_vector = np.arange(eb2, sb2, -1*stepb2)\n",
    "    b1_mat, b2_mat = np.meshgrid(b1_vector, b2_vector)\n",
    "    \n",
    "    for ib1, b1 in enumerate(b1_vector):                        # calculate the MSE for all possible model parameters\n",
    "        for ib2, b2 in enumerate(b2_vector):\n",
    "            y_hat = np.reshape(b1 * X1 + b2 * X2,newshape=[n])\n",
    "            RSS_mat[ib2,ib1] =((y - y_hat) ** 2).sum()\n",
    "            ridge_reg_mat[ib2,ib1] = b1*b1 + b2*b2\n",
    "            ridge_loss_mat[ib2,ib1] = RSS_mat[ib2,ib1]+ridge_lam*ridge_reg_mat[ib2,ib1]\n",
    "            \n",
    "            lasso_reg_mat[ib2,ib1] = abs(b1) + abs(b2)\n",
    "            lasso_loss_mat[ib2,ib1] = RSS_mat[ib2,ib1]+lasso_lam*lasso_reg_mat[ib2,ib1]\n",
    "    \n",
    "    ### find the solution\n",
    "    iridge_b1 = (np.absolute(b1_vector-ridge_b1)).argmin(); iridge_b2 = (np.absolute(b2_vector-ridge_b2)).argmin()\n",
    "    ridge_RSS = RSS_mat[iridge_b2,iridge_b1]; ridge_reg = ridge_reg_mat[iridge_b2,iridge_b1]\n",
    "    \n",
    "    ilasso_b1 = (np.absolute(b1_vector-lasso_b1)).argmin(); ilasso_b2 = (np.absolute(b2_vector-lasso_b2)).argmin()\n",
    "    lasso_RSS = RSS_mat[ilasso_b2,ilasso_b1]; lasso_reg = lasso_reg_mat[ilasso_b2,ilasso_b1]\n",
    "    \n",
    "    plt.subplot(121)\n",
    "    \n",
    "    vmin = np.percentile(RSS_mat.flatten(),q=1); vmax = np.percentile(RSS_mat.flatten(),q=20)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    plt.contour(b1_mat,b2_mat,RSS_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(0.8,0.2,5),\n",
    "                colors='darkred',linestyles='dotted',alpha=0.4,zorder=10)\n",
    "    vmin = np.percentile(ridge_reg_mat.flatten(),q=1); vmax = np.percentile(ridge_reg_mat.flatten(),q=20)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    plt.contour(b1_mat,b2_mat,ridge_reg_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(0.8,0.2,5),\n",
    "                colors='#cc5500',alpha=0.4,zorder=10)\n",
    "    \n",
    "    plt.contour(b1_mat,b2_mat,RSS_mat,levels = [ridge_RSS],linewidths=2.0,\n",
    "                 colors='darkred',alpha=0.7,zorder=10)\n",
    "    plt.scatter([linear_b1],[linear_b2],color='darkred',marker='x',s=50,zorder=100)\n",
    "    \n",
    "    plt.contour(b1_mat,b2_mat,ridge_reg_mat,levels = [ridge_reg],linewidths=2.0,\n",
    "                 colors='#cc5500',alpha=0.7,zorder=10)\n",
    "    plt.scatter([0.0],[0.0],color='#cc5500',marker='x',s=50,zorder=100)\n",
    "    \n",
    "    plt.scatter([ridge_b1],[ridge_b2],color='blue',edgecolor='black',marker='o',s=30,zorder=1000)\n",
    "    plt.plot(ridge_mult_b1,ridge_mult_b2,color='black',lw=1)\n",
    "    \n",
    "    plt.annotate('Global \\n Mean',(0.8,-0.5),ha='center',color = '#cc5500')\n",
    "    plt.annotate('Ordinary Least \\n Square',(linear_b1 + 1.6,linear_b2 - 0.5), ha='center',color='darkred')\n",
    "    \n",
    "    plt.plot([ridge_b1,ridge_b1],[sb1,ridge_b2],color='blue',lw=1.0,ls='--')\n",
    "    plt.annotate(r'$b_1$ = ' + str(np.round(ridge_b1,1)),(ridge_b1+0.2,-6.0),ha='center',color = 'blue',rotation=270)\n",
    "    \n",
    "    plt.plot([sb1,ridge_b1],[ridge_b2,ridge_b2],color='blue',lw=1.0,ls='--')\n",
    "    plt.annotate(r'$b_2$ = ' + str(np.round(ridge_b2,1)),(-6.0,ridge_b2+0.2),ha='center',color = 'blue')\n",
    "    \n",
    "    plt.title(r'Ridge Regression RSS L2 and Regularization L2 for $\\lambda = $' + str(np.round(ridge_lam)))\n",
    "    plt.xlabel(r'$b_1$ Model Parameter'); plt.ylabel(r'$b_2$ Model Parameter'); add_grid() \n",
    "    plt.xlim([sb1,eb1]); plt.ylim([sb2,eb2])\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    \n",
    "    vmin = np.percentile(RSS_mat.flatten(),q=1); vmax = np.percentile(RSS_mat.flatten(),q=20)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    plt.contour(b1_mat,b2_mat,RSS_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(0.8,0.2,5),\n",
    "                colors='darkred',linestyles='dotted',alpha=0.4,zorder=10)\n",
    "    vmin = np.percentile(lasso_reg_mat.flatten(),q=1); vmax = np.percentile(lasso_reg_mat.flatten(),q=20)\n",
    "    lvmin = math.log10(vmin); lvmax = math.log10(vmax)\n",
    "    plt.contour(b1_mat,b2_mat,lasso_reg_mat,levels = np.logspace(lvmin,lvmax,5),linewidths=np.linspace(0.8,0.2,5),\n",
    "                colors='#cc5500',alpha=0.4,zorder=10)\n",
    "    \n",
    "    plt.contour(b1_mat,b2_mat,RSS_mat,levels = [lasso_RSS],linewidths=2.0,\n",
    "                 colors='darkred',alpha=0.7,zorder=10)\n",
    "    plt.scatter([linear_b1],[linear_b2],color='darkred',marker='x',s=50,zorder=100)\n",
    "    \n",
    "    plt.contour(b1_mat,b2_mat,lasso_reg_mat,levels = [lasso_reg],linewidths=2.0,\n",
    "                 colors='#cc5500',alpha=0.7,zorder=10)\n",
    "    plt.scatter([0.0],[0.0],color='#cc5500',marker='x',s=50,zorder=100)\n",
    "    \n",
    "    plt.scatter([lasso_b1],[lasso_b2],color='blue',edgecolor='black',marker='o',s=30,zorder=1000)\n",
    "    plt.plot(lasso_mult_b1,lasso_mult_b2,color='black',lw=1)\n",
    "    \n",
    "    plt.annotate('Global \\n Mean',(0.8,-0.5),ha='center',color = '#cc5500')\n",
    "    plt.annotate('Ordinary Least \\n Square',(linear_b1 + 1.6,linear_b2 - 0.5), ha='center',color='darkred')\n",
    "    \n",
    "    plt.plot([lasso_b1,lasso_b1],[sb1,lasso_b2],color='blue',lw=1.0,ls='--')\n",
    "    plt.annotate(r'$b_1$ = ' + str(np.round(lasso_b1,1)),(lasso_b1+0.2,-6.0),ha='center',color = 'blue',rotation=270)\n",
    "    \n",
    "    plt.plot([sb1,lasso_b1],[lasso_b2,lasso_b2],color='blue',lw=1.0,ls='--')\n",
    "    plt.annotate(r'$b_2$ = ' + str(np.round(lasso_b2,1)),(-6.0,lasso_b2+0.2),ha='center',color = 'blue')\n",
    "    \n",
    "    plt.title(r'LASSO Regression RSS L2 and Regularization L1 for $\\lambda = $' + str(np.round(lasso_lam,1)))\n",
    "    plt.xlabel(r'$b_1$ Model Parameter'); plt.ylabel(r'$b_2$ Model Parameter'); add_grid() \n",
    "    plt.xlim([sb1,eb1]); plt.ylim([sb2,eb2])\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.1); plt.show()\n",
    "    \n",
    "interactive_plot_summary2 = widgets.interactive_output(run_plot_summary2, {'slam':slam,})\n",
    "interactive_plot_summary.clear_output(wait = True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Ridge vs. LASSO Regression Regularization with Variable $\\lambda$ Demonstration\n",
    "\n",
    "* change the $\\lambda$ hyperparameter level and watch the solution shift from linear regression to the global mean, all model parameters, $b_{\\alpha} = 0.0, \\alpha = 1,\\ldots,m$. This demonstration based on 2 predictor features dataset and I use hyperparameter levels since ridge and LASSO respond quite differently to $\\lambda$ magnitude.\n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "558707475bc845768d45b311f242ff84",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "VBox(children=(Text(value='                                       Ridge L2 and LASSO L1 Regression Regularizat…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui_summary2, interactive_plot_summary2)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "These interactive dashboards were designed as an educational tool to assist in learning about hyperparameter tuning and model regularization. The dashboards include:\n",
    "\n",
    "* the loss function as surfaces for ridge regression to observe the impact of the hyperparameter, model regularization, on the solution. \n",
    "\n",
    "* the loss function components for OLS and regularization for ridge regression and LASSO regression to observe the impact the hyperparameter on the solution.\n",
    "\n",
    "Specifically we can observe the balancing of the ordinary least square solution, data fit only, and regularization, lowest absolute (closest to zero) possible model parameters.\n",
    "\n",
    "I hope this is helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On twitter I'm the @GeostatsGuy.\n",
    "\n",
    "***\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Geostatistics:\n",
    "\n",
    "### Michael Pyrcz, Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  }
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